Main Results and Findings
Adding an interaction term to the model changes the interpretation of all the coefficients. Without an interaction term, we interpret B1 as the unique effect of man years on consultation
Final rmdarkdown (fixed)
Group 4 - Izolda Vlasova, Willem Ofstad, Hedvig Monsen Kristoffersen, Christine Diane Malaca Morte and Xhensila Kllogjeri
2022-08-04
Introduction
The Office of the Auditor General has assigned our group to assess the efficiency of hospitals in Norway. As the main auditing institution for the Norwegian government, it is important to see that the resources (taxes) collected is spent well. Previous investigations have indicated that there are large differences in efficiency and access to care between regions and hospitals.
Using quality indicators from the Norwegian Health Directorate we can assess quality improvements from year to year. Statistics Norway provides data on various accounts which are used as input factors for hospitals. We derive measurements of productivity from these variables
Brainstorming
The Office of the Auditor General is interested in keeping efficiency, productivity and quality as high as possible. This is in the interest of optimizing the distribution of the state-budget.
he data collection is done through web-APIs, API stands for Application Programming Interface which is a system, or interface, by which we can communicate with a server and ask to pull data from a hosting server.
#Data collection
Statistics Norway really tries to make obtaining data as simply as possible, there is a API console that helps find the right dataset and helps generate the correct instructions (queries) that will pull exactly the data that one wants.
After repeatedly running this code to import the data, we started thinking that maybe it would be nice to only need to pull data once and then save the data on the computer, so as to not unnecessarily bother the servers.
There was at one time a rather grandiose plan of only needing the links to the tables which would be enough to retrieve all possible meta-data for the data set, and setting it so that there was little filtering. This would make it so that we would get a big data set saved to our computer that we could filter from within our R-script. Below is how the code looked like at one point. For simplicity sake I made each query into seperate objects, but one of the ideas was that these queries could have been values within a list that with enough if statements within a for-loop would generate a very large data set. One of the issues, which could have been solved given enough time, is that the servers won’t answer queries that contain more than 800 000 cells/observations. This could have been side-stepped by simply pulling each year individually and rebuilding the data-set.
Data from SSB looked tidy and there were no missing values. Data from Helse Direktoratet was not as tidy and needed some transforming and deleting rows with a lot of missing values.
We used the function clean_names() to make the column names’ syntax consistent. Dates from Helse Direktoratet were in a wrong format, so we had to use the format() function to change it to only year.
We filtered the data using the filter() function to get the columns with the right variables and rows with the right hospitals. For some columns there were too few yearly observations so we removed the yearly filter and use the “tertialvis” period type and only chose the observations that are first in the year (another solution could be to use the mean() function on all the observations in the year).
Both datasets had columns that were not useful for our analysis, so we used the select() function to choose the columns with regions/hospitals names, year and value.
Then, we merged all the datasets from Helse Direktoratet and renamed the time column from “time_from” to “ar” for consistency with SSB dataset and so that it would be easier to merge the two big datasets. For SSB dataset, we renamed the “region” column into “location_name” for the same purpose. Some hospitals in Helse Direktoratet had too few observations so we deleted them with %notin% function. Examples of hospitals with a lot of missing values: private hospitals, closed hospitals.
For the health region data we devided the number of man-years by population of the health region using the mutate() function. The number of consultations, man-years, 24-hour stays and day treatments were changed so that they would be representing the number of services per citizen of the health region. The names of health regions were not consistent from SSB and Helse Direktoratet, so we had to mutate them so that they would be the same in both datasets.
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## Call:
## lm(formula = value_erfaringer ~ value_driftskostnader, data = super_merge_hospitals2019)
##
## Coefficients:
## (Intercept) value_driftskostnader
## 7.135e+01 3.190e-05##
## Call:
## lm(formula = value_erfaringer ~ value_driftskostnader, data = super_merge_hospitals_stavanger_hf)
##
## Coefficients:
## (Intercept) value_driftskostnader
## 62.803284 0.001145##
## Call:
## lm(formula = value_erfaringer ~ value_driftskostnader, data = super_merge_hospitals_stavanger_hf)
##
## Residuals:
## 1 2 3 4 7 10 11
## -0.2055 -0.8997 1.2240 0.6882 0.9105 -0.4633 -1.2544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.280e+01 2.581e+00 24.333 2.19e-06 ***
## value_driftskostnader 1.145e-03 4.227e-04 2.709 0.0423 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.043 on 5 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.5948, Adjusted R-squared: 0.5138
## F-statistic: 7.341 on 1 and 5 DF, p-value: 0.04231## tibble [277 x 16] (S3: tbl_df/tbl/data.frame)
## $ location_name : chr [1:277] "Helse Møre og Romsdal HF" "Helse Møre og Romsdal HF" "Helse Møre og Romsdal HF" "Helse Møre og Romsdal HF" ...
## $ value_reinleggelse : num [1:277] 15.5 14.6 16.1 15.2 15.4 ...
## $ ar : chr [1:277] "2013" "2012" "2015" "2014" ...
## $ value_overlevelse : num [1:277] 95.4 94.9 95.1 95.2 95.6 ...
## $ value_utsettelse : num [1:277] NA NA 7.6 NA 7.5 7.7 NA NA NA 7.2 ...
## $ value_korridor : num [1:277] 1.3 0.8 2 0.9 1.7 1 0.9 1 0.9 1.2 ...
## $ value_medvirkning : num [1:277] NA NA NA NA NA NA NA NA NA NA ...
## $ value_fristbrudd : num [1:277] 2 1.5 1.1 1.1 0.3 0.4 13 15.9 13.6 10.5 ...
## $ value_erfaringer : num [1:277] 71 NA 72 73 NA NA 72 69 72 74 ...
## $ value_fristbrudd_psykisk : num [1:277] 3.1 1.6 2 1 0 4 9.5 3.8 2.8 0 ...
## $ dagbehandlinger_oppholdsdager: int [1:277] 20839 21716 15263 23017 14172 14415 62924 61236 64584 51301 ...
## $ dognplasser : int [1:277] 748 770 772 790 763 752 1862 1851 1957 2009 ...
## $ liggedager_oppholdsdogn : int [1:277] 226255 235069 236656 244437 233650 223711 565866 596969 572885 540670 ...
## $ polikliniske_konsultasjoner : int [1:277] 413359 394335 468109 443036 469470 486350 976400 950938 987362 1046435 ...
## $ value_arsverk : int [1:277] 3949 3982 4551 4056 5080 5071 15119 15063 15792 16623 ...
## $ value_driftskostnader : int [1:277] 4930 4946 6073 4413 6071 6061 19409 18642 18651 22282 ...## 'data.frame': 48 obs. of 16 variables:
## $ ar : chr "2010" "2010" "2010" "2010" ...
## $ location_name : chr "Helseregion Midt-Norge" "Helseregion Nord" "Helseregion Sør-Øst (2007-)" "Helseregion Vest" ...
## $ value_arsverk : num 0.0184 0.0216 0.0167 0.0163 0.0189 ...
## $ value_dognplasser : num 3710 3912 3112 3546 3689 ...
## $ value_driftskostnader : int 194 245 185 171 223 280 214 195 234 292 ...
## $ dagbehandlinger : num 0.092 0.096 0.103 0.084 0.09 0.097 0.088 0.092 0.087 0.097 ...
## $ oppholdsdogn : num 1.15 1.2 1.11 1.22 1.13 ...
## $ konsultasjoner : num 1.53 1.47 1.42 1.22 1.58 ...
## $ value_reinleggelse : num NA NA NA NA NA NA NA NA 15.1 14.7 ...
## $ value_overlevelse : num 94.8 94.5 94.4 94.7 94.8 94.4 94.5 94.7 94.9 94.6 ...
## $ value_utsettelse : num NA NA NA NA NA NA NA NA NA NA ...
## $ value_korridor : num NA NA NA NA 0.7 2.1 1.7 3.4 0.6 1.9 ...
## $ value_medvirkning : num NA NA NA NA NA NA NA NA NA NA ...
## $ value_fristbrudd : num NA NA NA NA 3.8 11.3 8.4 2.7 1.7 6.1 ...
## $ value_erfaringer : num NA NA NA NA 71 69 69 69 73 70 ...
## $ value_fristbrudd_psykisk: num NA NA NA NA 1.8 17.5 9.2 7 0.5 19.5 ...## value_reinleggelse
## Min. :11.81
## 1st Qu.:14.78
## Median :15.60
## Mean :15.63
## 3rd Qu.:16.42
## Max. :20.03
## NA's :106
## ar
## Length:277
## Class :character
## Mode :character
## value_overlevelse
## Min. :92.80
## 1st Qu.:94.80
## Median :95.10
## Mean :95.11
## 3rd Qu.:95.50
## Max. :96.55
## NA's :59
## value_utsettelse
## Min. : 1.600
## 1st Qu.: 4.800
## Median : 6.240
## Mean : 6.274
## 3rd Qu.: 7.350
## Max. :12.500
## NA's :125
## value_korridor
## Min. :0.030
## 1st Qu.:0.660
## Median :1.200
## Mean :1.448
## 3rd Qu.:2.078
## Max. :4.800
## NA's :37
## value_medvirkning
## Min. : 1.00
## 1st Qu.:24.50
## Median :44.50
## Mean :38.62
## 3rd Qu.:50.00
## Max. :73.00
## NA's :235
## value_fristbrudd
## Min. : 0.000
## 1st Qu.: 0.400
## Median : 1.300
## Mean : 3.131
## 3rd Qu.: 4.075
## Max. :25.100
## NA's :37
## value_erfaringer
## Min. :64.00
## 1st Qu.:69.00
## Median :71.00
## Mean :71.07
## 3rd Qu.:73.00
## Max. :76.00
## NA's :131
## value_fristbrudd_psykisk
## Min. : 0.000
## 1st Qu.: 0.000
## Median : 0.600
## Mean : 3.067
## 3rd Qu.: 2.500
## Max. :35.300
## NA's :50
## dagbehandlinger_oppholdsdager
## Min. : 1
## 1st Qu.: 9738
## Median :17395
## Mean :18155
## 3rd Qu.:23247
## Max. :90204
## NA's :42
## dognplasser
## Min. : 159.0
## 1st Qu.: 432.5
## Median : 716.0
## Mean : 741.2
## 3rd Qu.: 981.0
## Max. :2155.0
## NA's :38
## liggedager_oppholdsdogn
## Min. : 37035
## 1st Qu.:114273
## Median :213698
## Mean :218678
## 3rd Qu.:307785
## Max. :742163
## NA's :38
## polikliniske_konsultasjoner
## Min. : 3285
## 1st Qu.: 191132
## Median : 377135
## Mean : 392538
## 3rd Qu.: 526354
## Max. :1142281
## NA's :38
## value_arsverk
## Min. : 475
## 1st Qu.: 2444
## Median : 4290
## Mean : 4704
## 3rd Qu.: 5996
## Max. :17782
## NA's :38
## value_driftskostnader
## Min. : 385
## 1st Qu.: 2909
## Median : 5012
## Mean : 6039
## 3rd Qu.: 7644
## Max. :26542
## NA's :38## Length Class Mode
## 48 character character
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01625 0.01848 0.01975 0.02041 0.02209 0.02682
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2507 2915 3110 3198 3505 4082
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 171.0 227.5 260.0 266.7 292.8 425.0
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.05200 0.05975 0.06750 0.07337 0.08825 0.10300
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6790 0.8395 0.9315 0.9523 1.0702 1.2230
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.220 1.536 1.666 1.762 2.086 2.301
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 12.88 15.18 15.78 15.58 16.23 16.93 16
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 94.30 94.80 95.11 95.16 95.60 96.20 8
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.094 5.475 5.950 6.119 6.647 8.000 20
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.4988 0.9000 1.3908 1.3691 1.7000 3.4000 4
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 18.00 31.25 41.50 38.25 47.00 54.00 40
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.200 0.700 1.650 3.264 4.525 15.100 4
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 68.00 70.00 71.00 71.43 73.00 75.00 20
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.100 0.475 1.950 4.100 5.000 25.000 4## Warning: package 'plotly' was built under R version 4.1.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:httr':
##
## config## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout## Warning: Ignoring 38 observations## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode## Warning: Ignoring 38 observations
## Warning: n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning: n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode## Warning: Ignoring 42 observations
## Warning: n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning: n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode## Warning: Ignoring 141 observations
## Warning: n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning: n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## 72998.93 67.92##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_driftskostnader
## 83593.36 51.16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_erfaringer,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_erfaringer
## -911412 18290##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 76413.37 30.83 28.33##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## -166316.77 36.23 22.78
## value_erfaringer
## 3467.77##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## -4.278e+04
## value_arsverk
## -6.693e+02
## value_driftskostnader
## 5.990e+02
## value_erfaringer
## 1.487e+02
## value_arsverk:value_driftskostnader
## -4.234e-03
## value_arsverk:value_erfaringer
## 9.940e+00
## value_driftskostnader:value_erfaringer
## -7.631e+00
## value_arsverk:value_driftskostnader:value_erfaringer
## 2.936e-05##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -197108 -22918 -1190 32985 101048
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.278e+04 3.602e+05
## value_arsverk -6.693e+02 2.874e+02
## value_driftskostnader 5.990e+02 2.285e+02
## value_erfaringer 1.487e+02 5.053e+03
## value_arsverk:value_driftskostnader -4.234e-03 6.395e-03
## value_arsverk:value_erfaringer 9.940e+00 4.035e+00
## value_driftskostnader:value_erfaringer -7.631e+00 3.201e+00
## value_arsverk:value_driftskostnader:value_erfaringer 2.936e-05 8.856e-05
## t value Pr(>|t|)
## (Intercept) -0.119 0.90565
## value_arsverk -2.329 0.02143 *
## value_driftskostnader 2.621 0.00983 **
## value_erfaringer 0.029 0.97656
## value_arsverk:value_driftskostnader -0.662 0.50914
## value_arsverk:value_erfaringer 2.463 0.01510 *
## value_driftskostnader:value_erfaringer -2.384 0.01858 *
## value_arsverk:value_driftskostnader:value_erfaringer 0.332 0.74076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52360 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9541, Adjusted R-squared: 0.9516
## F-statistic: 379.9 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -3.682e+04 4.083e+01
## value_driftskostnader value_arsverk:value_driftskostnader
## 5.489e+01 -2.142e-03##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader +
## value_arsverk * value_erfaringer + value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## 4.366e+04 -6.757e+02
## value_driftskostnader value_erfaringer
## 5.758e+02 -1.054e+03
## value_arsverk:value_driftskostnader value_arsverk:value_erfaringer
## -2.114e-03 1.002e+01
## value_driftskostnader:value_erfaringer
## -7.305e+00##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader +
## value_arsverk * value_erfaringer + value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -197124 -23417 -744 31930 101517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.366e+04 2.477e+05 0.176 0.86040
## value_arsverk -6.757e+02 2.858e+02 -2.365 0.01953
## value_driftskostnader 5.758e+02 2.168e+02 2.655 0.00892
## value_erfaringer -1.054e+03 3.506e+03 -0.301 0.76421
## value_arsverk:value_driftskostnader -2.114e-03 1.806e-04 -11.709 < 2e-16
## value_arsverk:value_erfaringer 1.002e+01 4.013e+00 2.497 0.01377
## value_driftskostnader:value_erfaringer -7.305e+00 3.036e+00 -2.407 0.01752
##
## (Intercept)
## value_arsverk *
## value_driftskostnader **
## value_erfaringer
## value_arsverk:value_driftskostnader ***
## value_arsverk:value_erfaringer *
## value_driftskostnader:value_erfaringer *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52180 on 129 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.954, Adjusted R-squared: 0.9519
## F-statistic: 446.2 on 6 and 129 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -2.101e+05 3.662e+01
## value_driftskostnader value_erfaringer
## 5.574e+01 2.552e+03
## value_arsverk:value_driftskostnader
## -2.090e-03##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader,
## data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -194251 -19538 -1925 33241 109694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.101e+05 1.272e+05 -1.651 0.101
## value_arsverk 3.662e+01 9.042e+00 4.050 8.72e-05 ***
## value_driftskostnader 5.574e+01 7.462e+00 7.470 1.01e-11 ***
## value_erfaringer 2.552e+03 1.803e+03 1.416 0.159
## value_arsverk:value_driftskostnader -2.090e-03 1.793e-04 -11.658 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53030 on 131 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9518, Adjusted R-squared: 0.9503
## F-statistic: 646.4 on 4 and 131 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader +
## value_erfaringer * value_driftskostnader, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -1.513e+05 3.752e+01
## value_driftskostnader value_erfaringer
## 4.390e+01 1.721e+03
## value_arsverk:value_driftskostnader value_driftskostnader:value_erfaringer
## -2.102e-03 1.576e-01##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader +
## value_erfaringer * value_driftskostnader, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -194256 -20521 -1210 33496 109589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.513e+05 2.398e+05 -0.631 0.529233
## value_arsverk 3.752e+01 9.592e+00 3.912 0.000147
## value_driftskostnader 4.390e+01 4.158e+01 1.056 0.293051
## value_erfaringer 1.721e+03 3.391e+03 0.508 0.612638
## value_arsverk:value_driftskostnader -2.102e-03 1.841e-04 -11.416 < 2e-16
## value_driftskostnader:value_erfaringer 1.576e-01 5.444e-01 0.290 0.772584
##
## (Intercept)
## value_arsverk ***
## value_driftskostnader
## value_erfaringer
## value_arsverk:value_driftskostnader ***
## value_driftskostnader:value_erfaringer
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53220 on 130 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9518, Adjusted R-squared: 0.95
## F-statistic: 513.5 on 5 and 130 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## 2644.193 3.248##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_driftskostnader
## 4304.025 2.259##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_erfaringer,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_erfaringer
## 19339.143 -8.906##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk +
## value_driftskostnader, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 2092.620 9.304 -4.627##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk +
## value_driftskostnader + value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 37147.818 9.642 -4.788
## value_erfaringer
## -496.922##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader * value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## 2.195e+04
## value_arsverk
## -6.968e+00
## value_driftskostnader
## 9.906e+00
## value_erfaringer
## -3.106e+02
## value_arsverk:value_driftskostnader
## 3.083e-04
## value_arsverk:value_erfaringer
## 2.255e-01
## value_driftskostnader:value_erfaringer
## -1.919e-01
## value_arsverk:value_driftskostnader:value_erfaringer
## -4.775e-06##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader * value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11927.1 -2124.5 248.9 2434.7 9639.0
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 2.195e+04 2.760e+04
## value_arsverk -6.968e+00 2.202e+01
## value_driftskostnader 9.906e+00 1.751e+01
## value_erfaringer -3.106e+02 3.871e+02
## value_arsverk:value_driftskostnader 3.083e-04 4.900e-04
## value_arsverk:value_erfaringer 2.255e-01 3.092e-01
## value_driftskostnader:value_erfaringer -1.919e-01 2.452e-01
## value_arsverk:value_driftskostnader:value_erfaringer -4.775e-06 6.785e-06
## t value Pr(>|t|)
## (Intercept) 0.795 0.428
## value_arsverk -0.316 0.752
## value_driftskostnader 0.566 0.573
## value_erfaringer -0.802 0.424
## value_arsverk:value_driftskostnader 0.629 0.530
## value_arsverk:value_erfaringer 0.729 0.467
## value_driftskostnader:value_erfaringer -0.783 0.435
## value_arsverk:value_driftskostnader:value_erfaringer -0.704 0.483
##
## Residual standard error: 4012 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9049, Adjusted R-squared: 0.8997
## F-statistic: 173.9 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -1.759e+02 9.506e+00
## value_driftskostnader value_arsverk:value_driftskostnader
## -4.113e+00 -4.131e-05##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## 53901.14 35.03##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_driftskostnader
## 69299.68 24.74##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_erfaringer
## 149538 1051##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk + value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 49166.54 86.46 -39.28##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk + value_driftskostnader +
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 368712.05 90.85 -42.02
## value_erfaringer
## -4531.53##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## -8.956e+04
## value_arsverk
## 3.813e+02
## value_driftskostnader
## -1.512e+02
## value_erfaringer
## 1.044e+03
## value_arsverk:value_driftskostnader
## -4.957e-03
## value_arsverk:value_erfaringer
## -4.110e+00
## value_driftskostnader:value_erfaringer
## 1.824e+00
## value_arsverk:value_driftskostnader:value_erfaringer
## 5.275e-05##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78767 -20223 -35 23335 61839
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -8.956e+04 2.138e+05
## value_arsverk 3.813e+02 1.706e+02
## value_driftskostnader -1.512e+02 1.356e+02
## value_erfaringer 1.044e+03 2.999e+03
## value_arsverk:value_driftskostnader -4.957e-03 3.795e-03
## value_arsverk:value_erfaringer -4.110e+00 2.395e+00
## value_driftskostnader:value_erfaringer 1.824e+00 1.899e+00
## value_arsverk:value_driftskostnader:value_erfaringer 5.275e-05 5.256e-05
## t value Pr(>|t|)
## (Intercept) -0.419 0.6760
## value_arsverk 2.235 0.0271 *
## value_driftskostnader -1.115 0.2669
## value_erfaringer 0.348 0.7283
## value_arsverk:value_driftskostnader -1.306 0.1939
## value_arsverk:value_erfaringer -1.716 0.0886 .
## value_driftskostnader:value_erfaringer 0.960 0.3387
## value_arsverk:value_driftskostnader:value_erfaringer 1.004 0.3174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31070 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.948, Adjusted R-squared: 0.9451
## F-statistic: 333.1 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -8.358e+03 9.153e+01
## value_driftskostnader value_arsverk:value_driftskostnader
## -2.579e+01 -1.088e-03##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## -4.278e+04
## value_arsverk
## -6.693e+02
## value_driftskostnader
## 5.990e+02
## value_erfaringer
## 1.487e+02
## value_arsverk:value_driftskostnader
## -4.234e-03
## value_arsverk:value_erfaringer
## 9.940e+00
## value_driftskostnader:value_erfaringer
## -7.631e+00
## value_arsverk:value_driftskostnader:value_erfaringer
## 2.936e-05##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -197108 -22918 -1190 32985 101048
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.278e+04 3.602e+05
## value_arsverk -6.693e+02 2.874e+02
## value_driftskostnader 5.990e+02 2.285e+02
## value_erfaringer 1.487e+02 5.053e+03
## value_arsverk:value_driftskostnader -4.234e-03 6.395e-03
## value_arsverk:value_erfaringer 9.940e+00 4.035e+00
## value_driftskostnader:value_erfaringer -7.631e+00 3.201e+00
## value_arsverk:value_driftskostnader:value_erfaringer 2.936e-05 8.856e-05
## t value Pr(>|t|)
## (Intercept) -0.119 0.90565
## value_arsverk -2.329 0.02143 *
## value_driftskostnader 2.621 0.00983 **
## value_erfaringer 0.029 0.97656
## value_arsverk:value_driftskostnader -0.662 0.50914
## value_arsverk:value_erfaringer 2.463 0.01510 *
## value_driftskostnader:value_erfaringer -2.384 0.01858 *
## value_arsverk:value_driftskostnader:value_erfaringer 0.332 0.74076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52360 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9541, Adjusted R-squared: 0.9516
## F-statistic: 379.9 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader * value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## 2.195e+04
## value_arsverk
## -6.968e+00
## value_driftskostnader
## 9.906e+00
## value_erfaringer
## -3.106e+02
## value_arsverk:value_driftskostnader
## 3.083e-04
## value_arsverk:value_erfaringer
## 2.255e-01
## value_driftskostnader:value_erfaringer
## -1.919e-01
## value_arsverk:value_driftskostnader:value_erfaringer
## -4.775e-06##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader * value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11927.1 -2124.5 248.9 2434.7 9639.0
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 2.195e+04 2.760e+04
## value_arsverk -6.968e+00 2.202e+01
## value_driftskostnader 9.906e+00 1.751e+01
## value_erfaringer -3.106e+02 3.871e+02
## value_arsverk:value_driftskostnader 3.083e-04 4.900e-04
## value_arsverk:value_erfaringer 2.255e-01 3.092e-01
## value_driftskostnader:value_erfaringer -1.919e-01 2.452e-01
## value_arsverk:value_driftskostnader:value_erfaringer -4.775e-06 6.785e-06
## t value Pr(>|t|)
## (Intercept) 0.795 0.428
## value_arsverk -0.316 0.752
## value_driftskostnader 0.566 0.573
## value_erfaringer -0.802 0.424
## value_arsverk:value_driftskostnader 0.629 0.530
## value_arsverk:value_erfaringer 0.729 0.467
## value_driftskostnader:value_erfaringer -0.783 0.435
## value_arsverk:value_driftskostnader:value_erfaringer -0.704 0.483
##
## Residual standard error: 4012 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9049, Adjusted R-squared: 0.8997
## F-statistic: 173.9 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## -8.956e+04
## value_arsverk
## 3.813e+02
## value_driftskostnader
## -1.512e+02
## value_erfaringer
## 1.044e+03
## value_arsverk:value_driftskostnader
## -4.957e-03
## value_arsverk:value_erfaringer
## -4.110e+00
## value_driftskostnader:value_erfaringer
## 1.824e+00
## value_arsverk:value_driftskostnader:value_erfaringer
## 5.275e-05##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78767 -20223 -35 23335 61839
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -8.956e+04 2.138e+05
## value_arsverk 3.813e+02 1.706e+02
## value_driftskostnader -1.512e+02 1.356e+02
## value_erfaringer 1.044e+03 2.999e+03
## value_arsverk:value_driftskostnader -4.957e-03 3.795e-03
## value_arsverk:value_erfaringer -4.110e+00 2.395e+00
## value_driftskostnader:value_erfaringer 1.824e+00 1.899e+00
## value_arsverk:value_driftskostnader:value_erfaringer 5.275e-05 5.256e-05
## t value Pr(>|t|)
## (Intercept) -0.419 0.6760
## value_arsverk 2.235 0.0271 *
## value_driftskostnader -1.115 0.2669
## value_erfaringer 0.348 0.7283
## value_arsverk:value_driftskostnader -1.306 0.1939
## value_arsverk:value_erfaringer -1.716 0.0886 .
## value_driftskostnader:value_erfaringer 0.960 0.3387
## value_arsverk:value_driftskostnader:value_erfaringer 1.004 0.3174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31070 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.948, Adjusted R-squared: 0.9451
## F-statistic: 333.1 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## 72998.93 67.92
##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_driftskostnader
## 83593.36 51.16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_erfaringer,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_erfaringer
## -911412 18290
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 38 rows containing non-finite values (stat_smooth).## Warning: Removed 38 rows containing missing values (geom_point).
##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 76413.37 30.83 28.33##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## -166316.77 36.23 22.78
## value_erfaringer
## 3467.77##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## -4.278e+04
## value_arsverk
## -6.693e+02
## value_driftskostnader
## 5.990e+02
## value_erfaringer
## 1.487e+02
## value_arsverk:value_driftskostnader
## -4.234e-03
## value_arsverk:value_erfaringer
## 9.940e+00
## value_driftskostnader:value_erfaringer
## -7.631e+00
## value_arsverk:value_driftskostnader:value_erfaringer
## 2.936e-05##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -197108 -22918 -1190 32985 101048
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.278e+04 3.602e+05
## value_arsverk -6.693e+02 2.874e+02
## value_driftskostnader 5.990e+02 2.285e+02
## value_erfaringer 1.487e+02 5.053e+03
## value_arsverk:value_driftskostnader -4.234e-03 6.395e-03
## value_arsverk:value_erfaringer 9.940e+00 4.035e+00
## value_driftskostnader:value_erfaringer -7.631e+00 3.201e+00
## value_arsverk:value_driftskostnader:value_erfaringer 2.936e-05 8.856e-05
## t value Pr(>|t|)
## (Intercept) -0.119 0.90565
## value_arsverk -2.329 0.02143 *
## value_driftskostnader 2.621 0.00983 **
## value_erfaringer 0.029 0.97656
## value_arsverk:value_driftskostnader -0.662 0.50914
## value_arsverk:value_erfaringer 2.463 0.01510 *
## value_driftskostnader:value_erfaringer -2.384 0.01858 *
## value_arsverk:value_driftskostnader:value_erfaringer 0.332 0.74076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52360 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9541, Adjusted R-squared: 0.9516
## F-statistic: 379.9 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -3.682e+04 4.083e+01
## value_driftskostnader value_arsverk:value_driftskostnader
## 5.489e+01 -2.142e-03##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader +
## value_arsverk * value_erfaringer + value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## 4.366e+04 -6.757e+02
## value_driftskostnader value_erfaringer
## 5.758e+02 -1.054e+03
## value_arsverk:value_driftskostnader value_arsverk:value_erfaringer
## -2.114e-03 1.002e+01
## value_driftskostnader:value_erfaringer
## -7.305e+00##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader +
## value_arsverk * value_erfaringer + value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -197124 -23417 -744 31930 101517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.366e+04 2.477e+05 0.176 0.86040
## value_arsverk -6.757e+02 2.858e+02 -2.365 0.01953
## value_driftskostnader 5.758e+02 2.168e+02 2.655 0.00892
## value_erfaringer -1.054e+03 3.506e+03 -0.301 0.76421
## value_arsverk:value_driftskostnader -2.114e-03 1.806e-04 -11.709 < 2e-16
## value_arsverk:value_erfaringer 1.002e+01 4.013e+00 2.497 0.01377
## value_driftskostnader:value_erfaringer -7.305e+00 3.036e+00 -2.407 0.01752
##
## (Intercept)
## value_arsverk *
## value_driftskostnader **
## value_erfaringer
## value_arsverk:value_driftskostnader ***
## value_arsverk:value_erfaringer *
## value_driftskostnader:value_erfaringer *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52180 on 129 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.954, Adjusted R-squared: 0.9519
## F-statistic: 446.2 on 6 and 129 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -2.101e+05 3.662e+01
## value_driftskostnader value_erfaringer
## 5.574e+01 2.552e+03
## value_arsverk:value_driftskostnader
## -2.090e-03##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader,
## data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -194251 -19538 -1925 33241 109694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.101e+05 1.272e+05 -1.651 0.101
## value_arsverk 3.662e+01 9.042e+00 4.050 8.72e-05 ***
## value_driftskostnader 5.574e+01 7.462e+00 7.470 1.01e-11 ***
## value_erfaringer 2.552e+03 1.803e+03 1.416 0.159
## value_arsverk:value_driftskostnader -2.090e-03 1.793e-04 -11.658 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53030 on 131 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9518, Adjusted R-squared: 0.9503
## F-statistic: 646.4 on 4 and 131 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader +
## value_erfaringer * value_driftskostnader, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -1.513e+05 3.752e+01
## value_driftskostnader value_erfaringer
## 4.390e+01 1.721e+03
## value_arsverk:value_driftskostnader value_driftskostnader:value_erfaringer
## -2.102e-03 1.576e-01##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk + value_driftskostnader +
## value_erfaringer + value_arsverk * value_driftskostnader +
## value_erfaringer * value_driftskostnader, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -194256 -20521 -1210 33496 109589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.513e+05 2.398e+05 -0.631 0.529233
## value_arsverk 3.752e+01 9.592e+00 3.912 0.000147
## value_driftskostnader 4.390e+01 4.158e+01 1.056 0.293051
## value_erfaringer 1.721e+03 3.391e+03 0.508 0.612638
## value_arsverk:value_driftskostnader -2.102e-03 1.841e-04 -11.416 < 2e-16
## value_driftskostnader:value_erfaringer 1.576e-01 5.444e-01 0.290 0.772584
##
## (Intercept)
## value_arsverk ***
## value_driftskostnader
## value_erfaringer
## value_arsverk:value_driftskostnader ***
## value_driftskostnader:value_erfaringer
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53220 on 130 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9518, Adjusted R-squared: 0.95
## F-statistic: 513.5 on 5 and 130 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## 2644.193 3.248##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_driftskostnader
## 4304.025 2.259##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_erfaringer,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_erfaringer
## 19339.143 -8.906##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk +
## value_driftskostnader, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 2092.620 9.304 -4.627##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk +
## value_driftskostnader + value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 37147.818 9.642 -4.788
## value_erfaringer
## -496.922##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader * value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## 2.195e+04
## value_arsverk
## -6.968e+00
## value_driftskostnader
## 9.906e+00
## value_erfaringer
## -3.106e+02
## value_arsverk:value_driftskostnader
## 3.083e-04
## value_arsverk:value_erfaringer
## 2.255e-01
## value_driftskostnader:value_erfaringer
## -1.919e-01
## value_arsverk:value_driftskostnader:value_erfaringer
## -4.775e-06##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader * value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11927.1 -2124.5 248.9 2434.7 9639.0
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 2.195e+04 2.760e+04
## value_arsverk -6.968e+00 2.202e+01
## value_driftskostnader 9.906e+00 1.751e+01
## value_erfaringer -3.106e+02 3.871e+02
## value_arsverk:value_driftskostnader 3.083e-04 4.900e-04
## value_arsverk:value_erfaringer 2.255e-01 3.092e-01
## value_driftskostnader:value_erfaringer -1.919e-01 2.452e-01
## value_arsverk:value_driftskostnader:value_erfaringer -4.775e-06 6.785e-06
## t value Pr(>|t|)
## (Intercept) 0.795 0.428
## value_arsverk -0.316 0.752
## value_driftskostnader 0.566 0.573
## value_erfaringer -0.802 0.424
## value_arsverk:value_driftskostnader 0.629 0.530
## value_arsverk:value_erfaringer 0.729 0.467
## value_driftskostnader:value_erfaringer -0.783 0.435
## value_arsverk:value_driftskostnader:value_erfaringer -0.704 0.483
##
## Residual standard error: 4012 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9049, Adjusted R-squared: 0.8997
## F-statistic: 173.9 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -1.759e+02 9.506e+00
## value_driftskostnader value_arsverk:value_driftskostnader
## -4.113e+00 -4.131e-05##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## 53901.14 35.03##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_driftskostnader
## 69299.68 24.74##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_erfaringer
## 149538 1051##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk + value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 49166.54 86.46 -39.28##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk + value_driftskostnader +
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk value_driftskostnader
## 368712.05 90.85 -42.02
## value_erfaringer
## -4531.53##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## -8.956e+04
## value_arsverk
## 3.813e+02
## value_driftskostnader
## -1.512e+02
## value_erfaringer
## 1.044e+03
## value_arsverk:value_driftskostnader
## -4.957e-03
## value_arsverk:value_erfaringer
## -4.110e+00
## value_driftskostnader:value_erfaringer
## 1.824e+00
## value_arsverk:value_driftskostnader:value_erfaringer
## 5.275e-05##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78767 -20223 -35 23335 61839
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -8.956e+04 2.138e+05
## value_arsverk 3.813e+02 1.706e+02
## value_driftskostnader -1.512e+02 1.356e+02
## value_erfaringer 1.044e+03 2.999e+03
## value_arsverk:value_driftskostnader -4.957e-03 3.795e-03
## value_arsverk:value_erfaringer -4.110e+00 2.395e+00
## value_driftskostnader:value_erfaringer 1.824e+00 1.899e+00
## value_arsverk:value_driftskostnader:value_erfaringer 5.275e-05 5.256e-05
## t value Pr(>|t|)
## (Intercept) -0.419 0.6760
## value_arsverk 2.235 0.0271 *
## value_driftskostnader -1.115 0.2669
## value_erfaringer 0.348 0.7283
## value_arsverk:value_driftskostnader -1.306 0.1939
## value_arsverk:value_erfaringer -1.716 0.0886 .
## value_driftskostnader:value_erfaringer 0.960 0.3387
## value_arsverk:value_driftskostnader:value_erfaringer 1.004 0.3174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31070 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.948, Adjusted R-squared: 0.9451
## F-statistic: 333.1 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader,
## data = super_merge)
##
## Coefficients:
## (Intercept) value_arsverk
## -8.358e+03 9.153e+01
## value_driftskostnader value_arsverk:value_driftskostnader
## -2.579e+01 -1.088e-03##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## -4.278e+04
## value_arsverk
## -6.693e+02
## value_driftskostnader
## 5.990e+02
## value_erfaringer
## 1.487e+02
## value_arsverk:value_driftskostnader
## -4.234e-03
## value_arsverk:value_erfaringer
## 9.940e+00
## value_driftskostnader:value_erfaringer
## -7.631e+00
## value_arsverk:value_driftskostnader:value_erfaringer
## 2.936e-05##
## Call:
## lm(formula = polikliniske_konsultasjoner ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -197108 -22918 -1190 32985 101048
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.278e+04 3.602e+05
## value_arsverk -6.693e+02 2.874e+02
## value_driftskostnader 5.990e+02 2.285e+02
## value_erfaringer 1.487e+02 5.053e+03
## value_arsverk:value_driftskostnader -4.234e-03 6.395e-03
## value_arsverk:value_erfaringer 9.940e+00 4.035e+00
## value_driftskostnader:value_erfaringer -7.631e+00 3.201e+00
## value_arsverk:value_driftskostnader:value_erfaringer 2.936e-05 8.856e-05
## t value Pr(>|t|)
## (Intercept) -0.119 0.90565
## value_arsverk -2.329 0.02143 *
## value_driftskostnader 2.621 0.00983 **
## value_erfaringer 0.029 0.97656
## value_arsverk:value_driftskostnader -0.662 0.50914
## value_arsverk:value_erfaringer 2.463 0.01510 *
## value_driftskostnader:value_erfaringer -2.384 0.01858 *
## value_arsverk:value_driftskostnader:value_erfaringer 0.332 0.74076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52360 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9541, Adjusted R-squared: 0.9516
## F-statistic: 379.9 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader * value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## 2.195e+04
## value_arsverk
## -6.968e+00
## value_driftskostnader
## 9.906e+00
## value_erfaringer
## -3.106e+02
## value_arsverk:value_driftskostnader
## 3.083e-04
## value_arsverk:value_erfaringer
## 2.255e-01
## value_driftskostnader:value_erfaringer
## -1.919e-01
## value_arsverk:value_driftskostnader:value_erfaringer
## -4.775e-06##
## Call:
## lm(formula = dagbehandlinger_oppholdsdager ~ value_arsverk *
## value_driftskostnader * value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11927.1 -2124.5 248.9 2434.7 9639.0
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 2.195e+04 2.760e+04
## value_arsverk -6.968e+00 2.202e+01
## value_driftskostnader 9.906e+00 1.751e+01
## value_erfaringer -3.106e+02 3.871e+02
## value_arsverk:value_driftskostnader 3.083e-04 4.900e-04
## value_arsverk:value_erfaringer 2.255e-01 3.092e-01
## value_driftskostnader:value_erfaringer -1.919e-01 2.452e-01
## value_arsverk:value_driftskostnader:value_erfaringer -4.775e-06 6.785e-06
## t value Pr(>|t|)
## (Intercept) 0.795 0.428
## value_arsverk -0.316 0.752
## value_driftskostnader 0.566 0.573
## value_erfaringer -0.802 0.424
## value_arsverk:value_driftskostnader 0.629 0.530
## value_arsverk:value_erfaringer 0.729 0.467
## value_driftskostnader:value_erfaringer -0.783 0.435
## value_arsverk:value_driftskostnader:value_erfaringer -0.704 0.483
##
## Residual standard error: 4012 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.9049, Adjusted R-squared: 0.8997
## F-statistic: 173.9 on 7 and 128 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Coefficients:
## (Intercept)
## -8.956e+04
## value_arsverk
## 3.813e+02
## value_driftskostnader
## -1.512e+02
## value_erfaringer
## 1.044e+03
## value_arsverk:value_driftskostnader
## -4.957e-03
## value_arsverk:value_erfaringer
## -4.110e+00
## value_driftskostnader:value_erfaringer
## 1.824e+00
## value_arsverk:value_driftskostnader:value_erfaringer
## 5.275e-05##
## Call:
## lm(formula = liggedager_oppholdsdogn ~ value_arsverk * value_driftskostnader *
## value_erfaringer, data = super_merge)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78767 -20223 -35 23335 61839
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -8.956e+04 2.138e+05
## value_arsverk 3.813e+02 1.706e+02
## value_driftskostnader -1.512e+02 1.356e+02
## value_erfaringer 1.044e+03 2.999e+03
## value_arsverk:value_driftskostnader -4.957e-03 3.795e-03
## value_arsverk:value_erfaringer -4.110e+00 2.395e+00
## value_driftskostnader:value_erfaringer 1.824e+00 1.899e+00
## value_arsverk:value_driftskostnader:value_erfaringer 5.275e-05 5.256e-05
## t value Pr(>|t|)
## (Intercept) -0.419 0.6760
## value_arsverk 2.235 0.0271 *
## value_driftskostnader -1.115 0.2669
## value_erfaringer 0.348 0.7283
## value_arsverk:value_driftskostnader -1.306 0.1939
## value_arsverk:value_erfaringer -1.716 0.0886 .
## value_driftskostnader:value_erfaringer 0.960 0.3387
## value_arsverk:value_driftskostnader:value_erfaringer 1.004 0.3174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31070 on 128 degrees of freedom
## (141 observations deleted due to missingness)
## Multiple R-squared: 0.948, Adjusted R-squared: 0.9451
## F-statistic: 333.1 on 7 and 128 DF, p-value: < 2.2e-16Limitations and steps forward
- User friendly Helse Direktoratet API package
- A more rigorous ‘big data’ approach that looked at more quality indicators using algorithms . Is production and efficiency optimized?